Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory113.3 B

Variable types

Categorical1
Numeric13

Alerts

mode has constant value "1"Constant
year is highly overall correlated with acousticness and 7 other fieldsHigh correlation
acousticness is highly overall correlated with year and 7 other fieldsHigh correlation
danceability is highly overall correlated with year and 6 other fieldsHigh correlation
duration_ms is highly overall correlated with speechinessHigh correlation
energy is highly overall correlated with year and 7 other fieldsHigh correlation
instrumentalness is highly overall correlated with year and 7 other fieldsHigh correlation
liveness is highly overall correlated with year and 5 other fieldsHigh correlation
loudness is highly overall correlated with year and 7 other fieldsHigh correlation
speechiness is highly overall correlated with duration_msHigh correlation
tempo is highly overall correlated with year and 6 other fieldsHigh correlation
popularity is highly overall correlated with year and 7 other fieldsHigh correlation
year is uniformly distributedUniform
year has unique valuesUnique
acousticness has unique valuesUnique
danceability has unique valuesUnique
duration_ms has unique valuesUnique
energy has unique valuesUnique
instrumentalness has unique valuesUnique
liveness has unique valuesUnique
loudness has unique valuesUnique
speechiness has unique valuesUnique
tempo has unique valuesUnique
valence has unique valuesUnique
popularity has unique valuesUnique
key has 34 (34.0%) zerosZeros

Reproduction

Analysis started2023-04-09 18:31:47.097507
Analysis finished2023-04-09 18:31:56.720536
Duration9.62 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

mode
Categorical

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-04-09T13:31:56.764335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T13:31:56.819055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

Most occurring characters

ValueCountFrequency (%)
1 100
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 100
100.0%

year
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.5
Minimum1921
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:56.872404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1921
5-th percentile1925.95
Q11945.75
median1970.5
Q31995.25
95-th percentile2015.05
Maximum2020
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.014722909
Kurtosis-1.2
Mean1970.5
Median Absolute Deviation (MAD)25
Skewness0
Sum197050
Variance841.66667
MonotonicityStrictly increasing
2023-04-09T13:31:56.944835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1921 1
 
1.0%
1984 1
 
1.0%
1994 1
 
1.0%
1993 1
 
1.0%
1992 1
 
1.0%
1991 1
 
1.0%
1990 1
 
1.0%
1989 1
 
1.0%
1988 1
 
1.0%
1987 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1921 1
1.0%
1922 1
1.0%
1923 1
1.0%
1924 1
1.0%
1925 1
1.0%
1926 1
1.0%
1927 1
1.0%
1928 1
1.0%
1929 1
1.0%
1930 1
1.0%
ValueCountFrequency (%)
2020 1
1.0%
2019 1
1.0%
2018 1
1.0%
2017 1
1.0%
2016 1
1.0%
2015 1
1.0%
2014 1
1.0%
2013 1
1.0%
2012 1
1.0%
2011 1
1.0%

acousticness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55631702
Minimum0.21993089
Maximum0.96260705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:57.016798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.21993089
5-th percentile0.2537523
Q10.2895157
median0.45918955
Q30.85671091
95-th percentile0.93680877
Maximum0.96260705
Range0.74267616
Interquartile range (IQR)0.5671952

Descriptive statistics

Standard deviation0.27535753
Coefficient of variation (CV)0.49496514
Kurtosis-1.7362644
Mean0.55631702
Median Absolute Deviation (MAD)0.20307254
Skewness0.23128849
Sum55.631702
Variance0.07582177
MonotonicityNot monotonic
2023-04-09T13:31:57.087349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.886896 1
 
1.0%
0.2877095803 1
 
1.0%
0.3062343631 1
 
1.0%
0.3090685537 1
 
1.0%
0.3208087638 1
 
1.0%
0.3327652916 1
 
1.0%
0.3328699506 1
 
1.0%
0.3133512518 1
 
1.0%
0.3220099465 1
 
1.0%
0.3116640664 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.2199308881 1
1.0%
0.2426866436 1
1.0%
0.2491917627 1
1.0%
0.2493126459 1
1.0%
0.2499530444 1
1.0%
0.2539522577 1
1.0%
0.2540809576 1
1.0%
0.2557635069 1
1.0%
0.2564705182 1
1.0%
0.257488086 1
1.0%
ValueCountFrequency (%)
0.9626070504 1
1.0%
0.9572467914 1
1.0%
0.9401998602 1
1.0%
0.9386165036 1
1.0%
0.9385915493 1
1.0%
0.9367149374 1
1.0%
0.9361794553 1
1.0%
0.9357705179 1
1.0%
0.922155 1
1.0%
0.9199445824 1
1.0%

danceability
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5367829
Minimum0.41444501
Maximum0.69290433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:57.161647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.41444501
5-th percentile0.45693787
Q10.50079969
median0.54097637
Q30.57094839
95-th percentile0.61384687
Maximum0.69290433
Range0.27845932
Interquartile range (IQR)0.070148703

Descriptive statistics

Standard deviation0.052355726
Coefficient of variation (CV)0.09753613
Kurtosis0.28621801
Mean0.5367829
Median Absolute Deviation (MAD)0.035510708
Skewness0.17246145
Sum53.67829
Variance0.0027411221
MonotonicityNot monotonic
2023-04-09T13:31:57.227761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4185973333 1
 
1.0%
0.53030895 1
 
1.0%
0.55282995 1
 
1.0%
0.56987815 1
 
1.0%
0.55506485 1
 
1.0%
0.555824359 1
 
1.0%
0.53529865 1
 
1.0%
0.54722745 1
 
1.0%
0.5404594359 1
 
1.0%
0.5410193333 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.4144450116 1
1.0%
0.4185973333 1
1.0%
0.4374260513 1
1.0%
0.4421994 1
1.0%
0.4551456338 1
1.0%
0.4570322 1
1.0%
0.4624801 1
1.0%
0.4633694737 1
1.0%
0.4646338882 1
1.0%
0.4656389 1
1.0%
ValueCountFrequency (%)
0.692904335 1
1.0%
0.6635004755 1
1.0%
0.6482682927 1
1.0%
0.6476698529 1
1.0%
0.6448141098 1
1.0%
0.6122170181 1
1.0%
0.6002023929 1
1.0%
0.5998802612 1
1.0%
0.5952217391 1
1.0%
0.5937740628 1
1.0%

duration_ms
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227296.75
Minimum156881.66
Maximum267677.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:57.299629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum156881.66
5-th percentile182013.67
Q1210889.19
median235520.85
Q3247702.74
95-th percentile257137.89
Maximum267677.82
Range110796.17
Interquartile range (IQR)36813.545

Descriptive statistics

Standard deviation25630.048
Coefficient of variation (CV)0.11276029
Kurtosis-0.36172577
Mean227296.75
Median Absolute Deviation (MAD)16539.906
Skewness-0.69361327
Sum22729675
Variance6.5689936 × 108
MonotonicityNot monotonic
2023-04-09T13:31:57.367059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260537.1667 1
 
1.0%
251845.2565 1
 
1.0%
250992.254 1
 
1.0%
251667.9675 1
 
1.0%
246506.46 1
 
1.0%
245912.0062 1
 
1.0%
256451.4035 1
 
1.0%
254203.5935 1
 
1.0%
257442.5897 1
 
1.0%
247543.3231 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
156881.6575 1
1.0%
165469.7465 1
1.0%
168999.4128 1
1.0%
171553.4255 1
1.0%
177942.3622 1
1.0%
182227.9445 1
1.0%
184986.9245 1
1.0%
184993.5984 1
1.0%
189356.1263 1
1.0%
191046.7076 1
1.0%
ValueCountFrequency (%)
267677.8231 1
1.0%
267676.967 1
1.0%
260537.1667 1
1.0%
260511.7935 1
1.0%
257442.5897 1
1.0%
257121.848 1
1.0%
257002.7905 1
1.0%
256451.4035 1
1.0%
254969.3715 1
1.0%
254708.4055 1
1.0%

energy
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45270542
Minimum0.2079478
Maximum0.6817778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:57.437340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2079478
5-th percentile0.23436269
Q10.28073344
median0.49599715
Q30.59800772
95-th percentile0.65675115
Maximum0.6817778
Range0.47383001
Interquartile range (IQR)0.31727428

Descriptive statistics

Standard deviation0.16173767
Coefficient of variation (CV)0.35726913
Kurtosis-1.6957336
Mean0.45270542
Median Absolute Deviation (MAD)0.15255102
Skewness-0.13296809
Sum45.270542
Variance0.026159073
MonotonicityNot monotonic
2023-04-09T13:31:57.511502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2318151333 1
 
1.0%
0.591091675 1
 
1.0%
0.578772685 1
 
1.0%
0.56255991 1
 
1.0%
0.582864205 1
 
1.0%
0.5594023641 1
 
1.0%
0.571591395 1
 
1.0%
0.584046375 1
 
1.0%
0.5856792462 1
 
1.0%
0.5798395538 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.2079477954 1
1.0%
0.2114670907 1
1.0%
0.2221723086 1
1.0%
0.2260444155 1
1.0%
0.2318151333 1
1.0%
0.2344967733 1
1.0%
0.2378153521 1
1.0%
0.2418007353 1
1.0%
0.2424648411 1
1.0%
0.246114615 1
1.0%
ValueCountFrequency (%)
0.6817778026 1
1.0%
0.6714608208 1
1.0%
0.6707487551 1
1.0%
0.6683047744 1
1.0%
0.660165261 1
1.0%
0.6565714602 1
1.0%
0.6532085113 1
1.0%
0.6503262821 1
1.0%
0.6488679451 1
1.0%
0.6487954438 1
1.0%

instrumentalness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19358185
Minimum0.016375524
Maximum0.58170091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:57.583978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.016375524
5-th percentile0.076534681
Q10.10332288
median0.12764402
Q30.27670732
95-th percentile0.43473271
Maximum0.58170091
Range0.56532539
Interquartile range (IQR)0.17338444

Descriptive statistics

Standard deviation0.12248788
Coefficient of variation (CV)0.63274468
Kurtosis0.22330401
Mean0.19358185
Median Absolute Deviation (MAD)0.049284062
Skewness1.0591252
Sum19.358185
Variance0.015003282
MonotonicityNot monotonic
2023-04-09T13:31:57.654709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3448780589 1
 
1.0%
0.1451730397 1
 
1.0%
0.1118857599 1
 
1.0%
0.1152939654 1
 
1.0%
0.1006900831 1
 
1.0%
0.1150923398 1
 
1.0%
0.1258262681 1
 
1.0%
0.1131825446 1
 
1.0%
0.1171903802 1
 
1.0%
0.1019745175 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.01637552431 1
1.0%
0.05421712167 1
1.0%
0.06366209032 1
1.0%
0.07295726805 1
1.0%
0.07587207368 1
1.0%
0.07656955484 1
1.0%
0.07764024697 1
1.0%
0.07770147188 1
1.0%
0.07793403491 1
1.0%
0.08298056822 1
1.0%
ValueCountFrequency (%)
0.5817009136 1
1.0%
0.4948354801 1
1.0%
0.4861264096 1
1.0%
0.4492919654 1
1.0%
0.4449516393 1
1.0%
0.4341948697 1
1.0%
0.4182973612 1
1.0%
0.4098969235 1
1.0%
0.3928820479 1
1.0%
0.3913284987 1
1.0%

liveness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20822381
Minimum0.16845024
Maximum0.2643351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:57.728547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.16845024
5-th percentile0.18103817
Q10.19750861
median0.20607445
Q30.21849322
95-th percentile0.23878842
Maximum0.2643351
Range0.095884856
Interquartile range (IQR)0.020984611

Descriptive statistics

Standard deviation0.017903382
Coefficient of variation (CV)0.085981435
Kurtosis0.16692249
Mean0.20822381
Median Absolute Deviation (MAD)0.011734826
Skewness0.37425784
Sum20.822381
Variance0.00032053107
MonotonicityNot monotonic
2023-04-09T13:31:57.800963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.20571 1
 
1.0%
0.1977062 1
 
1.0%
0.1921651 1
 
1.0%
0.19493225 1
 
1.0%
0.2002455 1
 
1.0%
0.1886289231 1
 
1.0%
0.1909609 1
 
1.0%
0.1990802 1
 
1.0%
0.2077075385 1
 
1.0%
0.2020181026 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.1684502439 1
1.0%
0.1726164187 1
1.0%
0.1752893735 1
1.0%
0.176325535 1
1.0%
0.1785354187 1
1.0%
0.1811698943 1
1.0%
0.18225715 1
1.0%
0.1827485641 1
1.0%
0.1870256359 1
1.0%
0.1882892821 1
1.0%
ValueCountFrequency (%)
0.2643351 1
1.0%
0.2490322 1
1.0%
0.2407197183 1
1.0%
0.2392107042 1
1.0%
0.2391016 1
1.0%
0.2387719415 1
1.0%
0.2376679856 1
1.0%
0.237111093 1
1.0%
0.2360002101 1
1.0%
0.2352190678 1
1.0%

loudness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.969054
Minimum-19.275282
Maximum-6.595067
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size928.0 B
2023-04-09T13:31:57.870934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-19.275282
5-th percentile-16.924759
Q1-14.189232
median-11.773061
Q3-9.9505421
95-th percentile-7.0663686
Maximum-6.595067
Range12.680215
Interquartile range (IQR)4.2386903

Descriptive statistics

Standard deviation3.1056098
Coefficient of variation (CV)-0.25946994
Kurtosis-0.80488325
Mean-11.969054
Median Absolute Deviation (MAD)2.4002023
Skewness0.030565325
Sum-1196.9054
Variance9.6448121
MonotonicityNot monotonic
2023-04-09T13:31:57.945128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-17.04866667 1
 
1.0%
-11.5232385 1
 
1.0%
-10.282273 1
 
1.0%
-10.882701 1
 
1.0%
-10.5754045 1
 
1.0%
-10.95576872 1
 
1.0%
-11.3274795 1
 
1.0%
-11.38413 1
 
1.0%
-11.51982103 1
 
1.0%
-11.62346718 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
-19.27528169 1
1.0%
-18.49253846 1
1.0%
-17.19198255 1
1.0%
-17.04866667 1
1.0%
-16.9814715 1
1.0%
-16.92177436 1
1.0%
-16.53037605 1
1.0%
-16.5160942 1
1.0%
-15.958866 1
1.0%
-15.812066 1
1.0%
ValueCountFrequency (%)
-6.595066995 1
1.0%
-6.843804092 1
1.0%
-6.909904266 1
1.0%
-7.044535897 1
1.0%
-7.046014796 1
1.0%
-7.0674399 1
1.0%
-7.168785069 1
1.0%
-7.260549614 1
1.0%
-7.265500513 1
1.0%
-7.466158974 1
1.0%

speechiness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10586109
Minimum0.049098399
Maximum0.49000074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:58.020128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.049098399
5-th percentile0.055914597
Q10.064243787
median0.085762806
Q30.1044383
95-th percentile0.28033276
Maximum0.49000074
Range0.44090234
Interquartile range (IQR)0.040194512

Descriptive statistics

Standard deviation0.082127501
Coefficient of variation (CV)0.77580442
Kurtosis11.9142
Mean0.10586109
Median Absolute Deviation (MAD)0.021178192
Skewness3.3635598
Sum10.586109
Variance0.0067449264
MonotonicityNot monotonic
2023-04-09T13:31:58.090257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.073662 1
 
1.0%
0.0592648 1
 
1.0%
0.07604935 1
 
1.0%
0.07635055 1
 
1.0%
0.0730623 1
 
1.0%
0.07110158974 1
 
1.0%
0.0643453 1
 
1.0%
0.06393925 1
 
1.0%
0.06609989744 1
 
1.0%
0.05778964103 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.0490983992 1
1.0%
0.0516815 1
1.0%
0.05203615 1
1.0%
0.05487125 1
1.0%
0.054975 1
1.0%
0.05596405 1
1.0%
0.0569993 1
1.0%
0.05731935 1
1.0%
0.05755646154 1
1.0%
0.05773984211 1
1.0%
ValueCountFrequency (%)
0.4900007353 1
1.0%
0.4837036284 1
1.0%
0.4536189441 1
1.0%
0.3539123847 1
1.0%
0.3050973 1
1.0%
0.2790293636 1
1.0%
0.24295765 1
1.0%
0.1732832447 1
1.0%
0.1643593158 1
1.0%
0.1599114988 1
1.0%

tempo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.01567
Minimum100.88452
Maximum124.28313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:58.161583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100.88452
5-th percentile106.18737
Q1111.71863
median117.45555
Q3120.60664
95-th percentile123.18412
Maximum124.28313
Range23.398607
Interquartile range (IQR)8.8880184

Descriptive statistics

Standard deviation5.6696447
Coefficient of variation (CV)0.048869644
Kurtosis-0.52649696
Mean116.01567
Median Absolute Deviation (MAD)4.0789368
Skewness-0.62083002
Sum11601.567
Variance32.144871
MonotonicityNot monotonic
2023-04-09T13:31:58.234913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.5314933 1
 
1.0%
121.376662 1
 
1.0%
118.44517 1
 
1.0%
120.364936 1
 
1.0%
122.5862185 1
 
1.0%
119.3625744 1
 
1.0%
120.0627335 1
 
1.0%
120.5790015 1
 
1.0%
120.0244256 1
 
1.0%
120.8457072 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
100.8845211 1
1.0%
101.5314933 1
1.0%
103.1645551 1
1.0%
105.9639295 1
1.0%
106.0083977 1
1.0%
106.1967915 1
1.0%
106.7722617 1
1.0%
107.9151229 1
1.0%
108.0583611 1
1.0%
108.2322805 1
1.0%
ValueCountFrequency (%)
124.2831286 1
1.0%
124.0875164 1
1.0%
123.5702153 1
1.0%
123.5099335 1
1.0%
123.4638077 1
1.0%
123.169395 1
1.0%
122.985001 1
1.0%
122.5862185 1
1.0%
122.4568165 1
1.0%
122.3052628 1
1.0%

valence
Real number (ℝ)

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53212011
Minimum0.37932667
Maximum0.66372542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:58.316755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.37932667
5-th percentile0.43147643
Q10.4971743
median0.54150328
Q30.57008021
95-th percentile0.61697044
Maximum0.66372542
Range0.28439876
Interquartile range (IQR)0.072905915

Descriptive statistics

Standard deviation0.057809381
Coefficient of variation (CV)0.10863972
Kurtosis-0.12257241
Mean0.53212011
Median Absolute Deviation (MAD)0.033871794
Skewness-0.3410064
Sum53.212011
Variance0.0033419246
MonotonicityNot monotonic
2023-04-09T13:31:58.392761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3793266667 1
 
1.0%
0.55163871 1
 
1.0%
0.52664915 1
 
1.0%
0.55556845 1
 
1.0%
0.5590983 1
 
1.0%
0.5486552308 1
 
1.0%
0.52652685 1
 
1.0%
0.5483329 1
 
1.0%
0.5464853897 1
 
1.0%
0.5414511795 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.3793266667 1
1.0%
0.3982538283 1
1.0%
0.4164763112 1
1.0%
0.4291420513 1
1.0%
0.4304195 1
1.0%
0.431532059 1
1.0%
0.4320983688 1
1.0%
0.4369104572 1
1.0%
0.44134755 1
1.0%
0.443134962 1
1.0%
ValueCountFrequency (%)
0.6637254237 1
1.0%
0.6597004878 1
1.0%
0.6365298319 1
1.0%
0.6254924324 1
1.0%
0.621928777 1
1.0%
0.61670947 1
1.0%
0.6162376299 1
1.0%
0.6099818 1
1.0%
0.5994099855 1
1.0%
0.5980580513 1
1.0%

popularity
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.376065
Minimum0.14084507
Maximum65.256542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:58.640611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.14084507
5-th percentile0.79459556
Q13.2982003
median33.61925
Q344.943375
95-th percentile56.847937
Maximum65.256542
Range65.115697
Interquartile range (IQR)41.645175

Descriptive statistics

Standard deviation20.703197
Coefficient of variation (CV)0.75625176
Kurtosis-1.4075081
Mean27.376065
Median Absolute Deviation (MAD)17.395365
Skewness-0.025195719
Sum2737.6065
Variance428.62239
MonotonicityNot monotonic
2023-04-09T13:31:58.710740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6533333333 1
 
1.0%
37.7745 1
 
1.0%
45.0615 1
 
1.0%
42.9975 1
 
1.0%
42.994 1
 
1.0%
41.65589744 1
 
1.0%
40.7855 1
 
1.0%
39.1825 1
 
1.0%
39.60051282 1
 
1.0%
39.68512821 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.1408450704 1
1.0%
0.1708074534 1
1.0%
0.3403361345 1
1.0%
0.6533333333 1
1.0%
0.6610169492 1
1.0%
0.8016260163 1
1.0%
0.9267151767 1
1.0%
0.93 1
1.0%
1.126634958 1
1.0%
1.181690141 1
1.0%
ValueCountFrequency (%)
65.25654182 1
1.0%
64.30197044 1
1.0%
63.29624346 1
1.0%
63.26355422 1
1.0%
59.64718976 1
1.0%
56.7006079 1
1.0%
55.54314214 1
1.0%
54.04706478 1
1.0%
53.30738721 1
1.0%
52.73015873 1
1.0%

key
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.79
Minimum0
Maximum10
Zeros34
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2023-04-09T13:31:58.770687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5626996
Coefficient of variation (CV)0.94002627
Kurtosis-1.6259268
Mean3.79
Median Absolute Deviation (MAD)2
Skewness0.21826052
Sum379
Variance12.692828
MonotonicityNot monotonic
2023-04-09T13:31:58.816409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 34
34.0%
7 32
32.0%
2 13
 
13.0%
9 7
 
7.0%
1 6
 
6.0%
10 4
 
4.0%
5 4
 
4.0%
ValueCountFrequency (%)
0 34
34.0%
1 6
 
6.0%
2 13
 
13.0%
5 4
 
4.0%
7 32
32.0%
9 7
 
7.0%
10 4
 
4.0%
ValueCountFrequency (%)
10 4
 
4.0%
9 7
 
7.0%
7 32
32.0%
5 4
 
4.0%
2 13
 
13.0%
1 6
 
6.0%
0 34
34.0%

Interactions

2023-04-09T13:31:55.886821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.510255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.171128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.968433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.745384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.411403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.058775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.727651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.475844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.148982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.803738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.447182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.223776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.949673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.565430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.228715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.030321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.795708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.460687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.109160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.776250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.525878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.198177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.855049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.496201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.274459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.005226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.622612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.290251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.089803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.850102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.513990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.163812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.828703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.581756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.251310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.907882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.549649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.328581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.055128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.672573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.348940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.143573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.902615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.564143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.215743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.878365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.633028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.301100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.958136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.600207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.382953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.105502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.722983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.410172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.197160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.953340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.614727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.267710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.927395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.688180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.350264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.007287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.650644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.434644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.153130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.771483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.471491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.250792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.004740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.664008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.318228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.976536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.741474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.399751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.056095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.700036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.484756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.203533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.822840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.534639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.304762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.057308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.715392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.371713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.027122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.794494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.450722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.107846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.751498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.537314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.249576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.869836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.593602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.355426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.107301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.763897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.421097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.181427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.844260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.497871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.155326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.799730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.585683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.299088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.921422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.653787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.408639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.158861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.814303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.474296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.232389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.896255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.549049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.205368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.851043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.637308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.345920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:47.969485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.714810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.457917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.209077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.862968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.523870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.280182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.946095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.596877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.253419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.899792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.686284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.392865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.018175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.779895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.507256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.259378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.911362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.575293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.329942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.996059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.645712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.301079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.948516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.736970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.441061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.068171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.842611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.557537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.311092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.960157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.626014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.379867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.047679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.694684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.350425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.997937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.787705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:56.490332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.118954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:48.904963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:49.696078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:50.362275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.010441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:51.678306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:52.429072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.099564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:53.753462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:54.400090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.048400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-09T13:31:55.838665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-09T13:31:58.868797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
yearacousticnessdanceabilityduration_msenergyinstrumentalnesslivenessloudnessspeechinesstempovalencepopularitykey
year1.000-0.9380.5130.4440.920-0.921-0.6670.922-0.2800.792-0.2050.979-0.022
acousticness-0.9381.000-0.615-0.428-0.9240.9290.606-0.9010.268-0.8320.101-0.925-0.038
danceability0.513-0.6151.000-0.0330.573-0.598-0.4230.6280.2650.5740.1690.5750.211
duration_ms0.444-0.428-0.0331.0000.448-0.359-0.1770.395-0.5160.4590.0880.4280.232
energy0.920-0.9240.5730.4481.000-0.896-0.5620.964-0.3330.8950.0590.9240.089
instrumentalness-0.9210.929-0.598-0.359-0.8961.0000.598-0.9130.295-0.8140.026-0.9280.055
liveness-0.6670.606-0.423-0.177-0.5620.5981.000-0.5610.198-0.4540.297-0.6710.041
loudness0.922-0.9010.6280.3950.964-0.913-0.5611.000-0.2640.8690.0570.9350.043
speechiness-0.2800.2680.265-0.516-0.3330.2950.198-0.2641.000-0.403-0.284-0.2840.088
tempo0.792-0.8320.5740.4590.895-0.814-0.4540.869-0.4031.0000.2300.8170.092
valence-0.2050.1010.1690.0880.0590.0260.2970.057-0.2840.2301.000-0.1390.234
popularity0.979-0.9250.5750.4280.924-0.928-0.6710.935-0.2840.817-0.1391.000-0.021
key-0.022-0.0380.2110.2320.0890.0550.0410.0430.0880.0920.234-0.0211.000

Missing values

2023-04-09T13:31:56.565077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T13:31:56.672080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

modeyearacousticnessdanceabilityduration_msenergyinstrumentalnesslivenessloudnessspeechinesstempovalencepopularitykey
0119210.8868960.418597260537.1666670.2318150.3448780.205710-17.0486670.073662101.5314930.3793270.6533332
1119220.9385920.482042165469.7464790.2378150.4341950.240720-19.2752820.116655100.8845210.5355490.14084510
2119230.9572470.577341177942.3621620.2624060.3717330.227462-14.1292110.093949114.0107300.6254925.3891890
3119240.9402000.549894191046.7076270.3443470.5817010.235219-14.2313430.092089120.6895720.6637250.66101710
4119250.9626070.573863184986.9244600.2785940.4182970.237668-14.1464140.111918115.5219210.6219292.6043175
5119260.6608170.599880156881.6574750.2114670.3330930.232370-18.4925380.483704109.6480330.4369101.4223519
6119270.9361790.648268184993.5983740.2643210.3913280.168450-14.4223740.113610114.8465240.6597000.8016267
7119280.9386170.534288214827.9064230.2079480.4948350.175289-17.1919830.159911106.7722620.4957131.5257731
8119290.6014270.647670168999.4128150.2418010.2152040.236000-16.5303760.490001110.9483570.6365300.3403367
9119300.9367150.518176195150.2853430.3335240.3522060.221311-12.8692210.119910109.8711940.6162380.9267152
modeyearacousticnessdanceabilityduration_msenergyinstrumentalnesslivenessloudnessspeechinesstempovalencepopularitykey
90120110.2731830.552867236998.7873080.6483010.1037720.203309-7.5749860.087479121.4839970.47245453.3073872
91120120.2499530.570882245807.4575840.6565710.0852060.189733-7.2605500.081742121.7817360.46270952.6550137
92120130.2574880.571148242267.6614370.6455970.0983650.199631-7.4720390.093849120.8068290.45474154.0470651
93120140.2493130.589948233728.3147130.6487950.0765700.191822-7.0674400.084061122.3052630.46304955.5431420
94120150.2539520.593774230029.0466060.6270640.1067870.188856-7.6256390.096779120.1154110.43209856.7006087
95120160.2841710.600202221396.5102950.5928550.0939840.181170-8.0610560.104313118.6526300.43153259.6471900
96120170.2860990.612217211115.6967870.5904210.0970910.191713-8.3126300.110536117.2027400.41647663.2635541
97120180.2676330.663500206001.0071330.6024350.0542170.176326-7.1687850.127176121.9223080.44792163.2962431
98120190.2782990.644814201024.7880960.5932240.0776400.172616-7.7221920.121043120.2356440.45881865.2565421
99120200.2199310.692904193728.3975370.6312320.0163760.178535-6.5950670.141384124.2831290.50104864.3019701